• Lead the adoption and evolution of agentic development tools (with Claude Code as the primary tool) within multidisciplinary squads. • Define and disseminate AI-assisted development methodologies — based on Spec-Driven Development (Spec-Kit, BMAD) — adapting the lifecycle to each product’s context. • Design and implement agentic processes across the engineering workflow: AI-assisted code review, generation and maintenance of tests, assisted refactoring, living documentation, and PR automation. • Mentor peers with support, raising the team’s maturity in AI-assisted engineering. • Support the squad’s backlog organization, prioritizing technical debt, architectural risks, and AI automation opportunities.

• Design, build, and operate production-grade AI capabilities embedded into real enterprise delivery • Contribute to internal reusable assets: reference implementations, patterns, templates, delivery playbooks • Informally mentor more junior engineers • Collaborate closely with solution architects, platform engineers, and client stakeholders to translate real business problems into working AI designs

Role Description Rockstar is recruiting for an AI-native operations platform for patient access — the behind-the-scenes work of getting patients enrolled in assistance programs, verifying coverage, and moving cases through review. Their thesis is simple: AI handles the routine, humans handle the judgment calls. They're in build mode for their first deployment, a patient access infrastructure company serving emerging biopharma, with go-live targeted later in 2026. They're hiring one or two senior engineers to build this platform with them. Given the size of the team, each hire carries the weight of a founding engineer — you will own entire verticals of the product, make architectural calls that we live with for years, and ship code that real patient cases flow through starting this fall. They are looking for engineers who build with LLMs the way previous generations built with databases — as a first-class architectural primitive, not a bolt-on. What you'll own: - Designing and shipping the AI agents that run patient case lifecycle — application intake, document extraction, data validation, coverage verification, patient communication, missing-info follow-up — including the evaluation harness, confidence thresholds, and human-in-the-loop boundaries. - PHI-aware data architecture: de-identification pipeline that strips identifiers before inference, the tokenization layer that maps results back, and the audit trail that logs every model call. - Integrations with healthcare systems of record and dependent services – as well as the orchestration layer that coordinates across them. - The production bar: on-call, incident response, rollback strategy, and strict uptime SLAs. - Working directly with operations, IT, and clinical teams during discovery, UAT, and go-live — you'll be in the room, not behind a PM. Qualifications - 5+ years shipping production software, with meaningful time owning backend systems that mattered when they broke. - Deep expertise in at least one modern backend language (Python, TypeScript, Kotlin, Go, or similar) and the judgment to know when the choice matters and when it doesn't. - Comfort across the full backend picture: relational databases, cloud infrastructure (AWS preferred), containerized services, API design, and the operational discipline that comes with running regulated software. - A real sense for UX — you care how the thing feels to use, not just whether it works. You've sat with users, sweated the details, and pushed back on designs that looked good but didn't hold up in the workflow. - You've built something real with LLMs — orchestration, retrieval, tool use, structured output, evals, handling model failure modes. - Track record of taking an ambiguous problem, shaping it into an architecture, and shipping it. - You treat model behavior as something to test, measure, and govern. Requirements - Bonus: healthcare or other regulated-industry experience; prior early-stage or founding-engineer time; Salesforce ecosystem; eligibility engines, document extraction, health compliance or case-management workflows. - Geography: Eastern Time preferred, +/- 3 hours. What this role is not - It's not a research role. - It's not a prompt-tuning role. - It's not a "wrap an API and call it an agent" role. - It's also not a role for someone who needs a lot of scaffolding around them — they are small, the customer is counting on them, and the timeline is real.

• Lead and Scale a High-Performing Pod • Own the AI Orchestration Layer • Modernize the Developer Experience • Define Evaluation and Trust Standards • Lead Foundational Model Strategy • Be a Strategic Partner to Leadership